The invention relates to a single-image super-resolution reconstruction algorithm based on a multi-scale residual error learning network. In recent years, the convolutional neural network is widely applied to many visual tasks, and particularly, remarkable results are obtained in the field of single-image super-resolution reconstruction. Similarly, multi-scale feature extraction also achieves consistent performance improvement in the field. However, in the prior art, multi-scale features are extracted in a layered mode mostly, and with the increase of the depth and width of a network, the calculation complexity and the consumption of a memory can be greatly improved. In order to solve the problem, the invention provides a compact multi-scale residual error learning network, i.e., representing multi-scale characteristics in a residual error block. The model is composed of a feature extraction block, a multi-scale information block and a reconstruction block. In addition, due to the factthat the number of network layers is small and group convolution is used, the network has the advantage of being high in execution speed. Experimental results show that the method is superior to an existing method in time and performance.